Hyperspectral Image Classification Based on Domain Adaptation Broad Learning

Abstract

Hyperspectral images (HSI) are widely applied in numerous fields for their rich spatial and spectral information. However, in these applications, we always face the situation that the available labeled samples are limited or absent. Therefore, we propose an HSI classification method based on domain adaptation broad learning (DABL). First, according to the importance of the marginal and conditional distributions, the maximum mean discrepancy is used in mapped features to adapt these distributions between source and target domains. Meanwhile the manifold regularization is added to maintain the manifold structure of the input HSI data. Second, to further reduce the distribution difference and maintain manifold structure, the domain adaptation and manifold regularization are added to the output layer of DABL. Finally, the output weights can be easily calculated by the ridge regression theory. Experimental results on three real HSI datasets demonstrate the effectiveness of our proposed DABL.